Hardware Performance Characterization of a Closed-Form Complex-Domain Encoder: Compute, Memory, Energy, and Latency for Real-Time and Edge Deployment

v2 update (2026-06-01): Reproducibility ZIP added back to the latest version alongside the manuscript files, so that downloading from the concept DOI gives all materials in one place rather than requiring navigation to v1. This revised deposit contains the Paper 6 manuscript (PDF and DOCX) along with the supplementary reproducibility archive. The manuscript files were added in this revised version of the deposit; the supplementary ZIP file remains unchanged from the original deposit. Manuscript targets IEEE Transactions on Parallel and Distributed Systems (under preparation). Coverage. 10 pre-registered studies (Studies 79-82 from Phase XV; Study 90 from Phase XVIII; Study 92 from Phase XIX; Studies 95-97 from Phase XX of the spiral-domain encoder validation campaign). 30 hypotheses, 26 SUPPORTED (87%), 4 honest bounded negatives substantively interpreted. Substantive findings. NumPy CPU throughput plateau 47, 500 subj/s; PyTorch MPS GPU plateau 220, 000 subj/s; CPU/MPS crossover at K approximately 30; single-frame latency p99 = 12. 13 microseconds, p99. 9 = 34. 13 microseconds (29x under 1 ms surgical RT budget) ; linear T-scaling slope -0. 978 (vs theoretical -1. 0) ; 0. 07 mJ/subject on MPS GPU (4-400x more efficient than typical edge-ML inference) ; INT16 encoded-state and INT8 federated-coefficient compatibility for Cortex-M class embedded deployment. Contents. Manuscript (PDF and DOCX of the paper itself) ; supplementary reproducibility archive containing: README. md (submission-package map and reproduction instructions) ; preregistrations/ (frozen pre-registration. md documents with literal-threshold decision rules) ; reports/ (per-study. md verdict reports against frozen rules + phase summaries) ; runners/ (deterministic Python runners under PYTHONHASHSEED=0) ; rawdata/ (per-study CSV outputs and JSON verdict blocks) ; figures/ (manuscript figures at 300 DPI + figure-build script) ; code/ (encoder source code). Reproducibility. Full validation pipeline is reproducible end-to-end under PYTHONHASHSEED=0 on a standard Python 3. 9+ installation with NumPy 2. 0+ linked against the Apple Accelerate framework, plus PyTorch 2. 8+ with MPS backend for the GPU portions. Reference machine: Apple Silicon arm64 (M-series), macOS 14. See README. md for per-study run commands. Methodological discipline. Every hypothesis was pre-registered with externally anchored decision rules frozen prior to runner execution. Zero post-hoc threshold adjustments were applied. Honest bounded negatives are interpreted substantively rather than discarded. Related companion archives. Paper 1 (10. 5281/zenodo. 20129137), Paper 2 (10. 5281/zenodo. 20138786), Paper 3 (10. 5281/zenodo. 20139171), and the corresponding Papers 4, 5, 7, 8 archives in this same Zenodo collection (Paper 8 at 10. 5281/zenodo. 20466035).